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This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.

Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.

The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.

To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.

Investments in gender training for agricultural researchers have not attained expected outcomes, bringing into question the efficacy of training approaches used. New approaches for transformative gender training need to draw on lessons learned from previous courses. This chapter analyses short gender training courses identified using a scoping methodology. Selected courses offered between 2005 and 2015 for scientists in Eastern Africa were critiqued against a theoretical framework for transformative gender training. Also shared is a training model (Gender Responsive Researchers Equipped for Agricultural Transformation course) that addresses gaps in previous courses. The chapter identifies critical lessons for facilitating transformative gender training for non-gender research practitioners which include the need for inter-disciplinary grounding in the disciplines of gender and agriculture, having a phased course delivery and the value of continuous technical support during and after training. Gender training models should also allow for the deepening of gender awareness and consciousness by providing safe spaces for personal reflections on the root causes of gender inequalities and for the questioning of the internalized norms and biases.

The results of this study have significant policy implications for charting a new course toward enhancing agricultural productivity among Chinese farmers.

By establishing a rural household decision-making model based on the transfer market of farmland operation rights, this paper systematically analyzes the effects of land transfer-in and land transfer-out on the productivity (per labor income) of rural households. The authors conducted basic regression analysis and robustness tests using propensity score-matching and proxy variable approaches based on the micro survey data from rural households in 30 counties in 21 provinces/municipalities/autonomous regions in 2013.

After the completion of land transfer, the total productivity of rural households transferring in lands will increase with an increase in the agricultural productivity; the total productivity of rural households transferring out land will increase due to a rise in non-agricultural productivity and the absolute total productivity of rural households not involved in land transfer will remain unchanged.

Unlike previous literature, this paper discusses the impacts of land transfer-in and transfer-out on total productivity, agricultural productivity and non-agricultural productivity among various rural households (i.e. those transferring in land, transferring out land or which are self-sufficient).

The purpose of this study was to conduct a comprehensive analysis of the impact and its mechanism on the transfer of agricultural labor forces in the surrounding areas resulting from the establishment of a natural reserve, which holds great significance. The significance of this analysis is on the ecological protection of the natural reserve and the coordinated development of local social economy.

This study first performs an analysis on the impact and its mechanism on the establishment of the natural reserve on the transfer of agricultural labor forces from two aspects, which are push and pull factors. Then, based on county panel data in Jiangxi Province from 1995 to 2012, this study builds a generalized difference-in-difference model and performs an empirical study on the impact, heterogeneity and its mechanism on the establishment of the natural reserve on the transfer of agricultural labor forces.

The empirical analysis reveals that the establishment of natural reserves would significantly promote the transfer of agricultural labor forces to non-agricultural sectors. The robust test and placebo test with changed estimation methods verify the robust of the result. The result passes the parallel trend test and shows that the impact is most significant within one year after the implementation of the policy. From the mechanism analysis, the impact mainly comes from the “push” effect brought by the restricted development of agricultural production and primary industry on agricultural labor forces, and the “pull” effect brought by the development of local tertiary industry.

The conclusion of this study enriches the understanding of the internal mechanism between the establishment of natural reserves and the transfer of agricultural labor forces from the push and pull factors, and can provide reference for formulating policies to promote the coordinated development of natural reserve construction and regional social economy.

Global climate change characterized by an increase in temperature has become the focus of attention all over the world. China is a sensitive and significant area of global climate change. This paper specifically aims to examine the association between agricultural productivity and the climate change by using China’s provincial agricultural input–output data from 2000 to 2019 and the climatic data of the ground meteorological stations.

The authors used the three-stage spatial Durbin model (SDM) model and entropy method for analysis of collected data; further, the authors also empirically tested the climate change marginal effect on agricultural productivity by using ordinary least square and SDM approaches.

The results revealed that climate change has a significant negative effect on agricultural productivity, which showed significance in robustness tests, including index replacement, quantile regression and tail reduction. The results of this study also indicated that by subdividing the climatic factors, annual precipitation had no significant impact on the growth of agricultural productivity; further, other climatic variables, including wind speed and temperature, had a substantial adverse effect on agricultural productivity. The heterogeneity test showed that climatic changes ominously hinder agricultural productivity growth only in the western region of China, and in the eastern and central regions, climate change had no effect.

The findings of this study highlight the importance of various social connections of farm households in designing policies to improve their responses to climate change and expand land productivity in different regions. The study also provides a hypothetical approach to prioritize developing regions that need proper attention to improve crop productivity.

The paper explores the impact of climate change on agricultural productivity by using the climatic data of China. Empirical evidence previously missing in the body of knowledge will support governments and researchers to establish a mechanism to improve climate change mitigation tools in China.

This study aims to examine determinants of farmers’ use of climate-smart agricultural practices, specifically improved crop varieties, intercropping, improved livestock breeds and rainwater harvesting in Wadla district, northeast Ethiopia.

A cross-sectional household survey was used. A structured interview schedule for respondent households and checklists for key informants and focus group discussants were used. This study used both descriptive statistics and a multivariate probit econometric model to analyze the collected data. The model was used to compute factors influencing the use of climate-smart agricultural practices in the study area.

The results revealed that households adopted selected practices. The likelihood of farmers’ decisions to use improved crop varieties, intercropping, improved livestock breeds and rainwater harvesting was 85%, 52%, 69% and 59%, respectively. The joint probability of using these climate-smart agricultural practices was 23.7%. The model results confirmed that sex, level of education, livestock holding, access to credit, farm distance, market distance and training were significant factors that affected the use of climate-smart agricultural practices in the study area.

The present study used the most selected locally practiced interventions for climate-smart agriculture.

Evaluating the economic effects of climate change is a pivotal step for planning adaptation in developing countries. For Bangladesh, global warming has put it among the most vulnerable countries in the world to climate change, with increasing temperatures and sea-level rise. Hence, the purpose of this paper is to examine how climate change impacts the economy in Bangladesh in the case of climate scenarios.

Using a dynamic computable general equilibrium (CGE) model and three climate change scenarios, this paper assesses the economy-wide implications of climate change on Bangladesh’s economy and agriculture. It is clear from the examination of the CGE model that the impacts of climate change on agricultural sectors were felt more sharply, reducing output by −3.25% and −3.70%, respectively, and increasing imports by 1.22% and 1.53% in 2030 and 2050, compared to the baseline.

The findings reveal that, relative to baseline, agricultural output will decline by a range of −3.1% to −3.6% under the high climate scenario (higher temperatures and lower yields). A decrease in agricultural output results in declines in agricultural labor and household income. Household income falls in all categories, although it drops the most in urban less educated households with a range of −3.1% to −3.4%. On the other hand, consumption of commodities will fall by −0.11% to −0.13%, according to the findings. Although climate change impacts had a relatively small effect on gross domestic product, reducing it by −0.059% and −0.098% in 2030 and 2050, respectively.

As agricultural output, household consumption and income decline, it will impact the majority of the population’s health in Bangladesh by increasing malnutrition, hidden hunger, poverty, changing food environment, changing physical and mental health status and a changing health-care environment. Therefore, population health and food security will be a top socioeconomic and political concern for Bangladesh Government.

The examination of the dynamic CGE model is its originality. In conclusion, the evidence generated here can provide important information to policymakers and guide government policies that contribute to national development and the achievement of food security targets. It is also necessary to put more emphasis on climate change issues and address potential risks in the following years.

In this paper we examine the validity of the J-curve hypothesis in four Southeast Asian economies (Indonesia, Malaysia, the Philippines and Thailand) over the 1980–2017 period.

We employ the linear autoregressive distributed lags (ARDL) model that captures the dynamic relationships between the variables and additionally use the nonlinear ARDL model that considers the asymmetric effects of the real exchange rate changes.

The estimated models were diagnostically sound, and the variables were found to be cointegrated. However, with the exception of Malaysia, the short- and long-run relationships did not attest to the presence of the J-curve effect. The trade flows were affected asymmetrically in Malaysia and the Philippines, suggesting the appropriateness of nonlinear ARDL in these countries.

The previous research tended to examine the effects of the real exchange rate changes on the agricultural trade balance and specifically the J-curve effect (deterioration of the trade balance followed by its improvement) in the developed economies and rarely in the developing ones. In this paper, we address this omission.

This paper investigates the impact of bank credit on agricultural productivity in the Central African Economic and Monetary Community (CEMAC) from 1990 to 2019. Studies’ results on the impact of bank credit on agricultural productivity are not conclusive. The studies demonstrate diverse outcomes which are debatable. The results are conflicting.

Agricultural value added (AGRVA) to the gross domestic product (GDP) proxies agricultural productivity while domestic credit to the private sector by banks (DCPSB), broad money supply, land, inflation (INF), physical capital (PHKAP) and labour supply are explanatory variables. The autoregressive distributed lag technique is utilized.

The co-integration test results show a long-run co-integration among the variables. The findings disclose that DCPSB, land and PHKAP impact positively on the AGRVA. Broad money supply, INF and labour impact negatively on the AGRVA to the GDP.

The results suggest that the CEMAC governments should encourage effective ways to increase bank credit flow to private enterprises in the agricultural sector through efficient bank's intermediation.

The governments should create more agricultural banks and improve the operation of existing ones to ensure direct credit to agricultural activities. The Bank of Central African Economic and Monetary Community should apply aggressive policy which eliminates all the bottlenecks undermining credit flow to the private sector in mutualism with agricultural productivity.

The commercial banks should give more credit to private sector to mutually benefit the agricultural sector and the banking sector. The governments of the CEMAC economies should expand funding into the capital market which considerably boosts agricultural productivity.

Studies’ results on the impact of bank credit on agricultural productivity are not conclusive. The studies demonstrate diverse outcomes which are debatable. The results are conflicting; some reveal positive impacts, some show negative impacts and others indicate U-shape behaviour. Hence, research is required to fill the lacuna.

The authors examined the impact of the Market Facilitation Program (MFP) and Coronavirus Food Assistance Program (CFAP) payments to United States agricultural producers on non-real estate agricultural loans.

The authors used quarterly, state-level commercial bank data from 2016–2020 to estimate dynamic panel models.

The authors found MFP and CFAP payments not associated with the percentage of non-real estate agricultural loans with payments over 90 days late. However, these payments associated with the percentage of non-real estate agricultural loans with payments between 30 and 89 days late. The available data utilized cannot consider when producers received the actual payment and what they specifically did with those funds.

The contribution of this study is for US policymakers and agricultural lenders. The findings could be helpful in designing and implementing future ad hoc payment programs and provide an understanding of potential shortcomings of the current safety net for agricultural producers in the Farm Bill. Additionally, findings can assist agricultural lenders in predicting the impact of ad hoc payments on their distressed loan portfolios.